The objective of this notebook is to show how covid 19 is spreading differently in the different continent on the earth planet.
Load data and python package library
Exploratory Data Analysis
2.1 Descriptive Analysis
2.2 Visualization
2.3 Epidemiology terms: Lethality, Incidence,etc.
Geospatial analysis
Forecasting
Conclusion
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
/kaggle/input/covid19-data-from-john-hopkins-university/CONVENIENT_global_deaths.csv /kaggle/input/covid19-data-from-john-hopkins-university/RAW_global_deaths.csv /kaggle/input/covid19-data-from-john-hopkins-university/RAW_global_confirmed_cases.csv /kaggle/input/covid19-data-from-john-hopkins-university/CONVENIENT_us_deaths.csv /kaggle/input/covid19-data-from-john-hopkins-university/CONVENIENT_global_metadata.csv /kaggle/input/covid19-data-from-john-hopkins-university/RAW_us_confirmed_cases.csv /kaggle/input/covid19-data-from-john-hopkins-university/CONVENIENT_us_confirmed_cases.csv /kaggle/input/covid19-data-from-john-hopkins-university/CONVENIENT_global_confirmed_cases.csv /kaggle/input/covid19-data-from-john-hopkins-university/CONVENIENT_us_metadata.csv /kaggle/input/covid19-data-from-john-hopkins-university/RAW_us_deaths.csv
# import package
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels as sm
import folium as fl
#import vincent
from pathlib import Path
from sklearn.impute import SimpleImputer
import geopandas as gpd
import mapclassify as mpc
import warnings
import plotly.offline as py
import plotly.express as px
import cufflinks as cf
%matplotlib inline
pd.options.plotting.backend
#pd.plotting.register_matplotlib_converters()
gpd.plotting.plot_linestring_collection
py.init_notebook_mode(connected=False)
cf.set_config_file(offline=True)
sns.set()
warnings.filterwarnings('ignore')
raw_conf = pd.read_csv('/kaggle/input/covid19-data-from-john-hopkins-university/RAW_global_confirmed_cases.csv')
raw_deaths = pd.read_csv('/kaggle/input/covid19-data-from-john-hopkins-university/RAW_global_deaths.csv')
conv_conf = pd.read_csv('/kaggle/input/covid19-data-from-john-hopkins-university/CONVENIENT_global_confirmed_cases.csv')
conv_deaths = pd.read_csv('/kaggle/input/covid19-data-from-john-hopkins-university/CONVENIENT_global_deaths.csv')
meta_conf = pd.read_csv('/kaggle/input/covid19-data-from-john-hopkins-university/CONVENIENT_global_metadata.csv')
raw_conf.head()
| Country/Region | Province/State | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | ... | 12/21/20 | 12/22/20 | 12/23/20 | 12/24/20 | 12/25/20 | 12/26/20 | 12/27/20 | 12/28/20 | 12/29/20 | 12/30/20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | NaN | 33.93911 | 67.709953 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 50013 | 50190 | 50433 | 50655 | 50810 | 50886 | 51039 | 51280 | 51350 | 51405 |
| 1 | Albania | NaN | 41.15330 | 20.168300 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 53425 | 53814 | 54317 | 54827 | 55380 | 55755 | 56254 | 56572 | 57146 | 57727 |
| 2 | Algeria | NaN | 28.03390 | 1.659600 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 95659 | 96069 | 96549 | 97007 | 97441 | 97857 | 98249 | 98631 | 98988 | 99311 |
| 3 | Andorra | NaN | 42.50630 | 1.521800 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 7602 | 7633 | 7669 | 7699 | 7756 | 7806 | 7821 | 7875 | 7919 | 7983 |
| 4 | Angola | NaN | -11.20270 | 17.873900 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 16686 | 16802 | 16931 | 17029 | 17099 | 17149 | 17240 | 17296 | 17371 | 17433 |
5 rows × 348 columns
raw_deaths.tail()
| Country/Region | Province/State | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | ... | 12/21/20 | 12/22/20 | 12/23/20 | 12/24/20 | 12/25/20 | 12/26/20 | 12/27/20 | 12/28/20 | 12/29/20 | 12/30/20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 266 | Vietnam | NaN | 14.058324 | 108.277199 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 35 | 35 | 35 | 35 | 35 | 35 | 35 | 35 | 35 | 35 |
| 267 | West Bank and Gaza | NaN | 31.952200 | 35.233200 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1169 | 1198 | 1226 | 1243 | 1260 | 1281 | 1309 | 1332 | 1351 | 1371 |
| 268 | Yemen | NaN | 15.552727 | 48.516388 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 606 | 606 | 606 | 606 | 606 | 606 | 607 | 607 | 610 | 610 |
| 269 | Zambia | NaN | -13.133897 | 27.849332 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 375 | 379 | 380 | 381 | 381 | 382 | 383 | 384 | 385 | 386 |
| 270 | Zimbabwe | NaN | -19.015438 | 29.154857 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 322 | 326 | 330 | 339 | 341 | 341 | 349 | 354 | 359 | 360 |
5 rows × 348 columns
raw_conf.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 271 entries, 0 to 270 Columns: 348 entries, Country/Region to 12/30/20 dtypes: float64(2), int64(344), object(2) memory usage: 736.9+ KB
raw_deaths.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 271 entries, 0 to 270 Columns: 348 entries, Country/Region to 12/30/20 dtypes: float64(2), int64(344), object(2) memory usage: 736.9+ KB
conv_conf.tail()
| Country/Region | Afghanistan | Albania | Algeria | Andorra | Angola | Antigua and Barbuda | Argentina | Armenia | Australia | ... | United Kingdom.10 | Uruguay | Uzbekistan | Vanuatu | Venezuela | Vietnam | West Bank and Gaza | Yemen | Zambia | Zimbabwe | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 339 | 12/26/20 | 76.0 | 375.0 | 416.0 | 50.0 | 50.0 | 0.0 | 3713.0 | 586.0 | 0.0 | ... | 34693.0 | 370.0 | 98.0 | 0.0 | 348.0 | 1.0 | 1306.0 | 0.0 | 100.0 | 83.0 |
| 340 | 12/27/20 | 153.0 | 499.0 | 392.0 | 15.0 | 91.0 | 0.0 | 5030.0 | 485.0 | 0.0 | ... | 32340.0 | 510.0 | 85.0 | 0.0 | 365.0 | 1.0 | 1189.0 | 2.0 | 163.0 | 114.0 |
| 341 | 12/28/20 | 241.0 | 318.0 | 382.0 | 54.0 | 56.0 | 3.0 | 7216.0 | 114.0 | 0.0 | ... | 41385.0 | 578.0 | 81.0 | 0.0 | 320.0 | 10.0 | 1217.0 | 2.0 | 109.0 | 71.0 |
| 342 | 12/29/20 | 70.0 | 574.0 | 357.0 | 44.0 | 75.0 | 0.0 | 11650.0 | 348.0 | 0.0 | ... | 53135.0 | 656.0 | 75.0 | 0.0 | 225.0 | 3.0 | 1149.0 | 0.0 | 234.0 | 177.0 |
| 343 | 12/30/20 | 55.0 | 581.0 | 323.0 | 64.0 | 62.0 | 0.0 | 11765.0 | 582.0 | 0.0 | ... | 50023.0 | 518.0 | 78.0 | 0.0 | 260.0 | 2.0 | 1277.0 | 1.0 | 285.0 | 300.0 |
5 rows × 272 columns
conv_conf.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 344 entries, 0 to 343 Columns: 272 entries, Country/Region to Zimbabwe dtypes: float64(188), object(84) memory usage: 731.1+ KB
#confirmed case descriptive
raw_conf.iloc[:, -1].describe()
count 2.710000e+02 mean 3.051955e+05 std 1.473547e+06 min 0.000000e+00 25% 7.385000e+02 50% 8.972000e+03 75% 1.262875e+05 max 1.974047e+07 Name: 12/30/20, dtype: float64
#deaths case descriptive
raw_deaths.iloc[:, -1].describe()
count 271.000000 mean 6660.523985 std 28270.199908 min 0.000000 25% 6.000000 50% 124.000000 75% 1991.500000 max 342312.000000 Name: 12/30/20, dtype: float64
date = list(raw_conf.columns)[-1]# last date
country_confirmed = raw_conf.groupby('Country/Region')[date].agg('sum').reset_index()
#top 10 country most affected by covid 19
country_confirmed.sort_values(by=date, ascending=False).style.background_gradient('viridis')
| Country/Region | 12/30/20 | |
|---|---|---|
| 177 | US | 19740468 |
| 79 | India | 10266674 |
| 23 | Brazil | 7619200 |
| 141 | Russia | 3100018 |
| 62 | France | 2657624 |
| 181 | United Kingdom | 2440202 |
| 176 | Turkey | 2194272 |
| 85 | Italy | 2083689 |
| 161 | Spain | 1910218 |
| 66 | Germany | 1741153 |
| 37 | Colombia | 1626461 |
| 6 | Argentina | 1613928 |
| 114 | Mexico | 1413935 |
| 137 | Poland | 1281414 |
| 81 | Iran | 1218753 |
| 179 | Ukraine | 1076880 |
| 159 | South Africa | 1039161 |
| 135 | Peru | 1010496 |
| 123 | Netherlands | 798592 |
| 80 | Indonesia | 735124 |
| 46 | Czechia | 701622 |
| 16 | Belgium | 644242 |
| 140 | Romania | 627941 |
| 35 | Chile | 606950 |
| 82 | Iraq | 594442 |
| 32 | Canada | 577266 |
| 13 | Bangladesh | 512496 |
| 131 | Pakistan | 479715 |
| 136 | Philippines | 472532 |
| 166 | Switzerland | 447905 |
| 165 | Sweden | 437379 |
| 119 | Morocco | 437332 |
| 84 | Israel | 416584 |
| 138 | Portugal | 406051 |
| 149 | Saudi Arabia | 362601 |
| 9 | Austria | 357902 |
| 151 | Serbia | 334991 |
| 77 | Hungary | 319543 |
| 88 | Jordan | 293067 |
| 122 | Nepal | 260059 |
| 132 | Panama | 242744 |
| 87 | Japan | 231271 |
| 65 | Georgia | 225893 |
| 10 | Azerbaijan | 217636 |
| 52 | Ecuador | 211512 |
| 43 | Croatia | 208446 |
| 180 | United Arab Emirates | 206092 |
| 25 | Bulgaria | 201220 |
| 89 | Kazakhstan | 200443 |
| 15 | Belarus | 192361 |
| 97 | Lebanon | 177996 |
| 155 | Slovakia | 173228 |
| 51 | Dominican Republic | 169579 |
| 41 | Costa Rica | 168114 |
| 47 | Denmark | 161862 |
| 7 | Armenia | 158878 |
| 20 | Bolivia | 158372 |
| 93 | Kuwait | 150298 |
| 115 | Moldova | 143841 |
| 139 | Qatar | 143621 |
| 102 | Lithuania | 138219 |
| 68 | Greece | 137918 |
| 175 | Tunisia | 137216 |
| 70 | Guatemala | 137166 |
| 187 | West Bank and Gaza | 136736 |
| 53 | Egypt | 136644 |
| 130 | Oman | 128719 |
| 59 | Ethiopia | 123856 |
| 27 | Burma | 123740 |
| 76 | Honduras | 120912 |
| 156 | Slovenia | 119740 |
| 185 | Venezuela | 113121 |
| 21 | Bosnia and Herzegovina | 110985 |
| 107 | Malaysia | 110485 |
| 134 | Paraguay | 106958 |
| 100 | Libya | 99935 |
| 2 | Algeria | 99311 |
| 90 | Kenya | 96251 |
| 36 | China | 95876 |
| 12 | Bahrain | 92425 |
| 83 | Ireland | 90157 |
| 127 | Nigeria | 86576 |
| 128 | North Macedonia | 82794 |
| 94 | Kyrgyzstan | 80843 |
| 183 | Uzbekistan | 76985 |
| 91 | Korea, South | 60740 |
| 154 | Singapore | 58569 |
| 1 | Albania | 57727 |
| 67 | Ghana | 54771 |
| 0 | Afghanistan | 51405 |
| 92 | Kosovo | 50910 |
| 129 | Norway | 49010 |
| 118 | Montenegro | 47782 |
| 103 | Luxembourg | 46415 |
| 54 | El Salvador | 45960 |
| 162 | Sri Lanka | 42702 |
| 96 | Latvia | 39043 |
| 61 | Finland | 35858 |
| 178 | Uganda | 34677 |
| 8 | Australia | 28405 |
| 57 | Estonia | 27256 |
| 31 | Cameroon | 26277 |
| 121 | Namibia | 23333 |
| 163 | Sudan | 23316 |
| 42 | Cote d'Ivoire | 22366 |
| 45 | Cyprus | 22019 |
| 189 | Zambia | 20462 |
| 150 | Senegal | 18945 |
| 120 | Mozambique | 18485 |
| 182 | Uruguay | 18480 |
| 105 | Madagascar | 17714 |
| 4 | Angola | 17433 |
| 40 | Congo (Kinshasa) | 17376 |
| 22 | Botswana | 14700 |
| 112 | Mauritania | 14191 |
| 108 | Maldives | 13738 |
| 71 | Guinea | 13707 |
| 190 | Zimbabwe | 13625 |
| 169 | Tajikistan | 13296 |
| 86 | Jamaica | 12793 |
| 110 | Malta | 12665 |
| 29 | Cabo Verde | 11793 |
| 44 | Cuba | 11687 |
| 167 | Syria | 11344 |
| 17 | Belize | 10724 |
| 74 | Haiti | 9999 |
| 63 | Gabon | 9571 |
| 58 | Eswatini | 9146 |
| 142 | Rwanda | 8250 |
| 3 | Andorra | 7983 |
| 11 | Bahamas | 7857 |
| 174 | Trinidad and Tobago | 7132 |
| 109 | Mali | 7029 |
| 39 | Congo (Brazzaville) | 6908 |
| 171 | Thailand | 6690 |
| 26 | Burkina Faso | 6631 |
| 106 | Malawi | 6471 |
| 73 | Guyana | 6319 |
| 164 | Suriname | 6181 |
| 125 | Nicaragua | 6046 |
| 49 | Djibouti | 5824 |
| 78 | Iceland | 5754 |
| 55 | Equatorial Guinea | 5264 |
| 33 | Central African Republic | 4963 |
| 158 | Somalia | 4714 |
| 64 | Gambia | 3797 |
| 173 | Togo | 3611 |
| 160 | South Sudan | 3540 |
| 18 | Benin | 3251 |
| 126 | Niger | 3208 |
| 98 | Lesotho | 3094 |
| 153 | Sierra Leone | 2583 |
| 72 | Guinea-Bissau | 2452 |
| 147 | San Marino | 2384 |
| 124 | New Zealand | 2162 |
| 101 | Liechtenstein | 2104 |
| 188 | Yemen | 2097 |
| 34 | Chad | 2077 |
| 99 | Liberia | 1779 |
| 186 | Vietnam | 1456 |
| 56 | Eritrea | 1252 |
| 117 | Mongolia | 1215 |
| 148 | Sao Tome and Principe | 1014 |
| 116 | Monaco | 853 |
| 28 | Burundi | 809 |
| 168 | Taiwan* | 797 |
| 133 | Papua New Guinea | 780 |
| 38 | Comoros | 765 |
| 48 | Diamond Princess | 712 |
| 19 | Bhutan | 655 |
| 113 | Mauritius | 527 |
| 170 | Tanzania | 509 |
| 14 | Barbados | 372 |
| 30 | Cambodia | 366 |
| 144 | Saint Lucia | 340 |
| 152 | Seychelles | 256 |
| 5 | Antigua and Barbuda | 158 |
| 24 | Brunei | 157 |
| 69 | Grenada | 127 |
| 145 | Saint Vincent and the Grenadines | 115 |
| 50 | Dominica | 88 |
| 60 | Fiji | 49 |
| 172 | Timor-Leste | 44 |
| 95 | Laos | 41 |
| 143 | Saint Kitts and Nevis | 32 |
| 75 | Holy See | 27 |
| 157 | Solomon Islands | 17 |
| 104 | MS Zaandam | 9 |
| 111 | Marshall Islands | 4 |
| 146 | Samoa | 2 |
| 184 | Vanuatu | 1 |
country_deaths = raw_deaths.groupby('Country/Region')[date].agg('sum').reset_index()
#top 10 country most killed by covid 19
country_deaths.sort_values(by=date, ascending=False).style.background_gradient('viridis')
| Country/Region | 12/30/20 | |
|---|---|---|
| 177 | US | 342312 |
| 23 | Brazil | 193875 |
| 79 | India | 148738 |
| 114 | Mexico | 124897 |
| 85 | Italy | 73604 |
| 181 | United Kingdom | 72657 |
| 62 | France | 64508 |
| 141 | Russia | 55692 |
| 81 | Iran | 55095 |
| 161 | Spain | 50689 |
| 6 | Argentina | 43163 |
| 37 | Colombia | 42909 |
| 135 | Peru | 37574 |
| 66 | Germany | 33230 |
| 159 | South Africa | 28033 |
| 137 | Poland | 28019 |
| 80 | Indonesia | 21944 |
| 176 | Turkey | 20642 |
| 16 | Belgium | 19441 |
| 179 | Ukraine | 19058 |
| 35 | Chile | 16499 |
| 140 | Romania | 15596 |
| 32 | Canada | 15498 |
| 52 | Ecuador | 14023 |
| 82 | Iraq | 12808 |
| 46 | Czechia | 11429 |
| 123 | Netherlands | 11417 |
| 131 | Pakistan | 10105 |
| 77 | Hungary | 9429 |
| 136 | Philippines | 9230 |
| 20 | Bolivia | 9149 |
| 165 | Sweden | 8727 |
| 166 | Switzerland | 7594 |
| 53 | Egypt | 7576 |
| 13 | Bangladesh | 7531 |
| 25 | Bulgaria | 7515 |
| 119 | Morocco | 7355 |
| 138 | Portugal | 6830 |
| 149 | Saudi Arabia | 6214 |
| 9 | Austria | 6149 |
| 70 | Guatemala | 4803 |
| 68 | Greece | 4788 |
| 36 | China | 4781 |
| 175 | Tunisia | 4620 |
| 21 | Bosnia and Herzegovina | 4050 |
| 132 | Panama | 3975 |
| 43 | Croatia | 3860 |
| 88 | Jordan | 3815 |
| 84 | Israel | 3307 |
| 87 | Japan | 3243 |
| 151 | Serbia | 3163 |
| 76 | Honduras | 3111 |
| 115 | Moldova | 2960 |
| 7 | Armenia | 2807 |
| 89 | Kazakhstan | 2761 |
| 2 | Algeria | 2751 |
| 156 | Slovenia | 2665 |
| 27 | Burma | 2664 |
| 10 | Azerbaijan | 2609 |
| 128 | North Macedonia | 2488 |
| 65 | Georgia | 2481 |
| 51 | Dominican Republic | 2409 |
| 134 | Paraguay | 2242 |
| 83 | Ireland | 2226 |
| 0 | Afghanistan | 2181 |
| 41 | Costa Rica | 2171 |
| 155 | Slovakia | 2065 |
| 59 | Ethiopia | 1918 |
| 122 | Nepal | 1847 |
| 90 | Kenya | 1667 |
| 130 | Oman | 1497 |
| 163 | Sudan | 1468 |
| 100 | Libya | 1459 |
| 97 | Lebanon | 1456 |
| 102 | Lithuania | 1422 |
| 15 | Belarus | 1414 |
| 187 | West Bank and Gaza | 1371 |
| 94 | Kyrgyzstan | 1354 |
| 54 | El Salvador | 1327 |
| 92 | Kosovo | 1325 |
| 127 | Nigeria | 1278 |
| 47 | Denmark | 1256 |
| 1 | Albania | 1174 |
| 185 | Venezuela | 1025 |
| 93 | Kuwait | 933 |
| 8 | Australia | 909 |
| 91 | Korea, South | 900 |
| 167 | Syria | 704 |
| 118 | Montenegro | 681 |
| 180 | United Arab Emirates | 665 |
| 96 | Latvia | 626 |
| 183 | Uzbekistan | 614 |
| 188 | Yemen | 610 |
| 40 | Congo (Kinshasa) | 584 |
| 61 | Finland | 556 |
| 103 | Luxembourg | 495 |
| 107 | Malaysia | 463 |
| 31 | Cameroon | 448 |
| 129 | Norway | 436 |
| 4 | Angola | 405 |
| 150 | Senegal | 402 |
| 189 | Zambia | 386 |
| 190 | Zimbabwe | 360 |
| 12 | Bahrain | 352 |
| 112 | Mauritania | 339 |
| 67 | Ghana | 335 |
| 86 | Jamaica | 302 |
| 109 | Mali | 269 |
| 105 | Madagascar | 261 |
| 178 | Uganda | 251 |
| 139 | Qatar | 245 |
| 17 | Belize | 242 |
| 74 | Haiti | 236 |
| 57 | Estonia | 226 |
| 110 | Malta | 216 |
| 162 | Sri Lanka | 199 |
| 121 | Namibia | 196 |
| 106 | Malawi | 189 |
| 58 | Eswatini | 184 |
| 182 | Uruguay | 174 |
| 11 | Bahamas | 170 |
| 120 | Mozambique | 165 |
| 125 | Nicaragua | 165 |
| 73 | Guyana | 164 |
| 44 | Cuba | 145 |
| 42 | Cote d'Ivoire | 137 |
| 158 | Somalia | 130 |
| 174 | Trinidad and Tobago | 126 |
| 64 | Gambia | 124 |
| 164 | Suriname | 121 |
| 45 | Cyprus | 119 |
| 29 | Cabo Verde | 112 |
| 39 | Congo (Brazzaville) | 107 |
| 34 | Chad | 104 |
| 126 | Niger | 102 |
| 169 | Tajikistan | 90 |
| 55 | Equatorial Guinea | 86 |
| 142 | Rwanda | 86 |
| 26 | Burkina Faso | 84 |
| 3 | Andorra | 84 |
| 99 | Liberia | 83 |
| 71 | Guinea | 80 |
| 153 | Sierra Leone | 76 |
| 173 | Togo | 68 |
| 63 | Gabon | 64 |
| 160 | South Sudan | 63 |
| 33 | Central African Republic | 63 |
| 171 | Thailand | 61 |
| 49 | Djibouti | 61 |
| 147 | San Marino | 59 |
| 98 | Lesotho | 51 |
| 108 | Maldives | 48 |
| 72 | Guinea-Bissau | 45 |
| 18 | Benin | 44 |
| 22 | Botswana | 40 |
| 101 | Liechtenstein | 39 |
| 186 | Vietnam | 35 |
| 154 | Singapore | 29 |
| 78 | Iceland | 29 |
| 124 | New Zealand | 25 |
| 170 | Tanzania | 21 |
| 148 | Sao Tome and Principe | 17 |
| 48 | Diamond Princess | 13 |
| 113 | Mauritius | 10 |
| 133 | Papua New Guinea | 9 |
| 38 | Comoros | 9 |
| 168 | Taiwan* | 7 |
| 14 | Barbados | 7 |
| 5 | Antigua and Barbuda | 5 |
| 144 | Saint Lucia | 5 |
| 116 | Monaco | 3 |
| 24 | Brunei | 3 |
| 28 | Burundi | 2 |
| 104 | MS Zaandam | 2 |
| 60 | Fiji | 2 |
| 117 | Mongolia | 1 |
| 56 | Eritrea | 1 |
| 69 | Grenada | 0 |
| 157 | Solomon Islands | 0 |
| 19 | Bhutan | 0 |
| 75 | Holy See | 0 |
| 172 | Timor-Leste | 0 |
| 50 | Dominica | 0 |
| 143 | Saint Kitts and Nevis | 0 |
| 184 | Vanuatu | 0 |
| 30 | Cambodia | 0 |
| 145 | Saint Vincent and the Grenadines | 0 |
| 146 | Samoa | 0 |
| 152 | Seychelles | 0 |
| 111 | Marshall Islands | 0 |
| 95 | Laos | 0 |
plt.figure(figsize=(15,5))
sns.distplot(country_confirmed[date], bins=10)
plt.title(f'Covid19 confirmed case distribution for date {date}')
plt.show()
plt.figure(figsize=(15,5))
sns.distplot(country_deaths[date], bins=10)
plt.title(f'Covid19 deaths case distribution for date {date}')
plt.show()
sns.jointplot(x=raw_conf.Long, y=raw_conf. Lat, kind='hex')
plt.show()
confirmed = raw_conf.groupby('Country/Region')[list(raw_conf.columns)[4:]].agg('sum')
death = raw_deaths.groupby('Country/Region')[list(raw_conf.columns)[4:]].agg('sum')
case_conf = confirmed.T
case_conf[['US', 'India', 'Brazil', 'Russia','France','Germany']].iplot(title='6 countries having huge COVID-19 confirmed cases in the world.',
legend=True)
fatalities = death.T
fatalities[['US', 'India', 'Brazil', 'Mexico','Italy','Germany']].iplot(title='6 countries having huge COVID-19 deaths in the world.',
legend=True)
Put all together raw_conf and raw_death in the same dataframe global_covid19
region = []
cases = []
time = []
latitude = []
longitude = []
fat = []
for u in list(raw_conf.columns)[4:]:
time.append([u for i in range(raw_conf.shape[0])])
region.append(list(raw_conf['Country/Region']))
cases.append(list(raw_conf[u]))
latitude.append(list(raw_conf.Lat))
longitude.append(list(raw_conf.Long))
fat.append(list(raw_deaths[u]))
global_covid19 = pd.DataFrame()
global_covid19['date'] = np.concatenate(time)
global_covid19['country'] = np.concatenate(region)
global_covid19['Lat'] = np.concatenate(latitude)
global_covid19['Long'] = np.concatenate(longitude)
global_covid19['cases'] = np.concatenate(cases)
global_covid19['fatalities'] = np.concatenate(fat)
global_covid19.head()
| date | country | Lat | Long | cases | fatalities | |
|---|---|---|---|---|---|---|
| 0 | 1/22/20 | Afghanistan | 33.93911 | 67.709953 | 0 | 0 |
| 1 | 1/22/20 | Albania | 41.15330 | 20.168300 | 0 | 0 |
| 2 | 1/22/20 | Algeria | 28.03390 | 1.659600 | 0 | 0 |
| 3 | 1/22/20 | Andorra | 42.50630 | 1.521800 | 0 | 0 |
| 4 | 1/22/20 | Angola | -11.20270 | 17.873900 | 0 | 0 |
Interactive Map Visualisation
center_point = dict(lon=0, lat=0)
figx = px.density_mapbox(global_covid19, lat='Lat', lon='Long', z="cases",
center = center_point, hover_name='country', zoom = 5,
range_color= [20, 20] , radius=20,
mapbox_style= 'open-street-map', title='Novel COVID-19 cases in the world',
animation_frame='date')
figx.update(layout_coloraxis_showscale=True)
figx.show()
center = dict(lon=0, lat=0)
figy = px.density_mapbox(global_covid19, lat='Lat', lon='Long', z="fatalities",
center = center, hover_name='country', zoom = 5,
range_color= [20, 20] , radius=20,
mapbox_style= 'open-street-map', title='Novel COVID-19 fatalities in the world.',
animation_frame='date')
figy.update(layout_coloraxis_showscale=True)
figy.show()
replace = ['Dem. Rep. Congo', 'Congo','Central African Rep.',
'Eq. Guinea','eSwatini','Bosnia and Herz.', 'S. Sudan', 'Dominican Rep.',
'United States of America', 'South Korea', "Côte d'Ivoire"]
name = ['Congo (Kinshasa)', 'Congo (Brazzaville)',
'Central African Republic', 'Equatorial Guinea', 'Eswatini', 'Bosnia and Herzegovina', 'South Sudan',
'Dominica','US', 'Korea, South',"Côte d'Ivoire"]
global_covid19 = global_covid19.replace(to_replace=name, value=replace)
end_date = global_covid19.date.unique()[-1] #today
yesterday = global_covid19.date.unique()[-2] #yesterday
geoCovid = gpd.GeoDataFrame(global_covid19, geometry=gpd.points_from_xy(global_covid19.Long, global_covid19.Lat))
geoCovid.crs = 'epsg:4326'
geoCovid.head()
| date | country | Lat | Long | cases | fatalities | geometry | |
|---|---|---|---|---|---|---|---|
| 0 | 1/22/20 | Afghanistan | 33.93911 | 67.709953 | 0 | 0 | POINT (67.70995 33.93911) |
| 1 | 1/22/20 | Albania | 41.15330 | 20.168300 | 0 | 0 | POINT (20.16830 41.15330) |
| 2 | 1/22/20 | Algeria | 28.03390 | 1.659600 | 0 | 0 | POINT (1.65960 28.03390) |
| 3 | 1/22/20 | Andorra | 42.50630 | 1.521800 | 0 | 0 | POINT (1.52180 42.50630) |
| 4 | 1/22/20 | Angola | -11.20270 | 17.873900 | 0 | 0 | POINT (17.87390 -11.20270) |
world_path_file = gpd.datasets.get_path('naturalearth_lowres') # upload natural data map
world = gpd.read_file(world_path_file)
need_data = global_covid19[global_covid19.date == end_date]
before_data = global_covid19[global_covid19.date == yesterday]
before_data.head()
| date | country | Lat | Long | cases | fatalities | geometry | |
|---|---|---|---|---|---|---|---|
| 92682 | 12/29/20 | Afghanistan | 33.93911 | 67.709953 | 51350 | 2179 | POINT (67.70995 33.93911) |
| 92683 | 12/29/20 | Albania | 41.15330 | 20.168300 | 57146 | 1170 | POINT (20.16830 41.15330) |
| 92684 | 12/29/20 | Algeria | 28.03390 | 1.659600 | 98988 | 2745 | POINT (1.65960 28.03390) |
| 92685 | 12/29/20 | Andorra | 42.50630 | 1.521800 | 7919 | 84 | POINT (1.52180 42.50630) |
| 92686 | 12/29/20 | Angola | -11.20270 | 17.873900 | 17371 | 403 | POINT (17.87390 -11.20270) |
need_data['new_cases'] = need_data['cases'].values - before_data['cases'].values
need_data['new_fatalities'] = need_data['fatalities'].values - before_data['fatalities'].values
need_data.head()
| date | country | Lat | Long | cases | fatalities | geometry | new_cases | new_fatalities | |
|---|---|---|---|---|---|---|---|---|---|
| 92953 | 12/30/20 | Afghanistan | 33.93911 | 67.709953 | 51405 | 2181 | POINT (67.70995 33.93911) | 55 | 2 |
| 92954 | 12/30/20 | Albania | 41.15330 | 20.168300 | 57727 | 1174 | POINT (20.16830 41.15330) | 581 | 4 |
| 92955 | 12/30/20 | Algeria | 28.03390 | 1.659600 | 99311 | 2751 | POINT (1.65960 28.03390) | 323 | 6 |
| 92956 | 12/30/20 | Andorra | 42.50630 | 1.521800 | 7983 | 84 | POINT (1.52180 42.50630) | 64 | 0 |
| 92957 | 12/30/20 | Angola | -11.20270 | 17.873900 | 17433 | 405 | POINT (17.87390 -11.20270) | 62 | 2 |
need_dat = need_data.groupby('country')[['cases', 'fatalities','new_cases', 'new_fatalities']].agg('sum').reset_index()
geo_merged = world.merge(need_dat[['country','cases','fatalities', 'new_cases', 'new_fatalities']] ,
left_on='name', right_on='country')
geo_merged.tail()
| pop_est | continent | name | iso_a3 | gdp_md_est | geometry | country | cases | fatalities | new_cases | new_fatalities | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | 7111024 | Europe | Serbia | SRB | 101800.0 | POLYGON ((18.82982 45.90887, 18.82984 45.90888... | Serbia | 334991 | 3163 | 3236 | 44 |
| 156 | 642550 | Europe | Montenegro | MNE | 10610.0 | POLYGON ((20.07070 42.58863, 19.80161 42.50009... | Montenegro | 47782 | 681 | 378 | 4 |
| 157 | 1895250 | Europe | Kosovo | -99 | 18490.0 | POLYGON ((20.59025 41.85541, 20.52295 42.21787... | Kosovo | 50910 | 1325 | 0 | 0 |
| 158 | 1218208 | North America | Trinidad and Tobago | TTO | 43570.0 | POLYGON ((-61.68000 10.76000, -61.10500 10.890... | Trinidad and Tobago | 7132 | 126 | 5 | 1 |
| 159 | 13026129 | Africa | S. Sudan | SSD | 20880.0 | POLYGON ((30.83385 3.50917, 29.95350 4.17370, ... | S. Sudan | 3540 | 63 | 29 | 0 |
geo_merged['prevalence'] = (geo_merged.cases/geo_merged.pop_est)*100000
geo_merged['incidence'] = (geo_merged.new_cases/geo_merged.pop_est)*100000
geo_merged['lethality'] = (geo_merged.fatalities/geo_merged.cases)*100
geo_merged.tail()
| pop_est | continent | name | iso_a3 | gdp_md_est | geometry | country | cases | fatalities | new_cases | new_fatalities | prevalence | incidence | lethality | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | 7111024 | Europe | Serbia | SRB | 101800.0 | POLYGON ((18.82982 45.90887, 18.82984 45.90888... | Serbia | 334991 | 3163 | 3236 | 44 | 4710.868646 | 45.506807 | 0.944204 |
| 156 | 642550 | Europe | Montenegro | MNE | 10610.0 | POLYGON ((20.07070 42.58863, 19.80161 42.50009... | Montenegro | 47782 | 681 | 378 | 4 | 7436.308458 | 58.828107 | 1.425223 |
| 157 | 1895250 | Europe | Kosovo | -99 | 18490.0 | POLYGON ((20.59025 41.85541, 20.52295 42.21787... | Kosovo | 50910 | 1325 | 0 | 0 | 2686.189157 | 0.000000 | 2.602632 |
| 158 | 1218208 | North America | Trinidad and Tobago | TTO | 43570.0 | POLYGON ((-61.68000 10.76000, -61.10500 10.890... | Trinidad and Tobago | 7132 | 126 | 5 | 1 | 585.450104 | 0.410439 | 1.766685 |
| 159 | 13026129 | Africa | S. Sudan | SSD | 20880.0 | POLYGON ((30.83385 3.50917, 29.95350 4.17370, ... | S. Sudan | 3540 | 63 | 29 | 0 | 27.176147 | 0.222629 | 1.779661 |
geo_merged.plot(cmap='cividis_r', column='cases', legend=True, figsize=(15,9), scheme='quantiles', k=4)
plt.title(f'SARS-Cov 2 cases in the worldwide for date {end_date}.')
plt.show()
geo_merged.plot(cmap='OrRd', column='fatalities', legend=True, figsize=(15,9), scheme='quantiles', k=3)
plt.title(f'SARS-Cov 2 fatalities in the worldwide for date {end_date}.')
plt.show()
most_aff_country = need_data[need_data.country.isin(['United States of America', 'Brazil', 'India', 'France',
'Russia', 'United Kingdom', 'Turkey', 'Italy', 'Spain',
'Argentina', 'Colombia', 'Germany','Mexico', 'Poland',
'Iran', 'Peru', 'Ukraine', 'South Africa', 'Netherlands',
'Indonesia'])]
ax = world.plot(figsize=(20,20), edgecolor='black', linestyle=':', color='whitesmoke')
ax.scatter(most_aff_country.Long, most_aff_country.Lat, cmap='cividis', s=most_aff_country.cases/10000,
c=most_aff_country.cases )
for id in [10, 100, 150, 200]:
plt.scatter([], [], c='k', alpha=0.5, s=id, label = str(id))
plt.legend(scatterpoints=1, frameon=True, labelspacing=1, title='Confirmed size')
plt.title('The spreading of COVID-19 cases in the 20 countries most affected ')
plt.show()
sars_bilan = []
for u in list(geo_merged.continent.unique()):
x = geo_merged[geo_merged.continent == u]
c = x.groupby('continent')[['cases', 'fatalities']].agg('sum')
sars_bilan.append(c)
pd.concat(sars_bilan)['cases'].iplot(title='SARS-Cov 2 cases in the Earth planet', legend=True, logy=False, kind='bar')
pd.concat(sars_bilan)['fatalities'].iplot(title='SARS-Cov 2 fatalities in the Earth planet', legend=True, logy=False, kind='bar')
geo_merged[['country', 'prevalence']].sort_values(by='prevalence', ascending=False).style.background_gradient('viridis')
| country | prevalence | |
|---|---|---|
| 118 | Luxembourg | 7812.263309 |
| 156 | Montenegro | 7436.308458 |
| 140 | Czechia | 6572.741981 |
| 29 | Panama | 6467.754218 |
| 78 | Qatar | 6205.788601 |
| 137 | Slovenia | 6071.620170 |
| 3 | United States of America | 6043.756661 |
| 119 | Belgium | 5606.323228 |
| 117 | Switzerland | 5438.180213 |
| 79 | Kuwait | 5226.989291 |
| 99 | Armenia | 5217.341047 |
| 70 | Israel | 5019.262128 |
| 108 | Lithuania | 4894.684898 |
| 116 | Croatia | 4856.509467 |
| 155 | Serbia | 4710.868646 |
| 120 | Netherlands | 4674.305735 |
| 133 | Georgia | 4585.421602 |
| 100 | Sweden | 4391.140714 |
| 106 | Moldova | 4140.356654 |
| 104 | Austria | 4088.246693 |
| 39 | France | 3960.327875 |
| 122 | Spain | 3901.735766 |
| 130 | United Kingdom | 3767.519910 |
| 81 | Oman | 3758.892835 |
| 121 | Portugal | 3746.025883 |
| 25 | Brazil | 3674.499830 |
| 8 | Argentina | 3643.729989 |
| 9 | Chile | 3411.888753 |
| 28 | Colombia | 3409.877002 |
| 30 | Costa Rica | 3409.841838 |
| 77 | United Arab Emirates | 3393.871527 |
| 128 | Italy | 3353.335543 |
| 103 | Poland | 3330.400876 |
| 27 | Peru | 3255.814673 |
| 105 | Hungary | 3243.813094 |
| 139 | Slovakia | 3180.929846 |
| 35 | Belize | 2976.028595 |
| 107 | Romania | 2916.590629 |
| 129 | Denmark | 2887.326104 |
| 154 | Bosnia and Herz. | 2878.106603 |
| 76 | Jordan | 2859.728989 |
| 71 | Lebanon | 2857.173126 |
| 112 | Bulgaria | 2833.481893 |
| 114 | Turkey | 2714.164345 |
| 157 | Kosovo | 2686.189157 |
| 102 | Ukraine | 2445.571789 |
| 18 | Bahamas | 2380.995673 |
| 132 | Azerbaijan | 2184.794179 |
| 17 | Russia | 2179.159331 |
| 110 | Estonia | 2177.725613 |
| 111 | Germany | 2160.399822 |
| 101 | Belarus | 2014.304672 |
| 109 | Latvia | 2007.720697 |
| 21 | South Africa | 1894.842436 |
| 115 | Albania | 1893.938524 |
| 146 | Cyprus | 1802.547421 |
| 123 | Ireland | 1799.145178 |
| 131 | Iceland | 1693.613189 |
| 2 | Canada | 1620.455832 |
| 143 | Paraguay | 1540.351675 |
| 80 | Iraq | 1516.738917 |
| 149 | Libya | 1502.056902 |
| 97 | Iran | 1485.893393 |
| 26 | Bolivia | 1421.877113 |
| 96 | Kyrgyzstan | 1396.463920 |
| 32 | Honduras | 1337.708426 |
| 40 | Ecuador | 1298.343438 |
| 147 | Morocco | 1286.775648 |
| 113 | Greece | 1280.756787 |
| 145 | Saudi Arabia | 1269.088334 |
| 74 | Tunisia | 1203.248040 |
| 23 | Mexico | 1135.008892 |
| 4 | Kazakhstan | 1080.165232 |
| 38 | Suriname | 1044.230714 |
| 45 | Namibia | 939.036856 |
| 19 | Norway | 921.232809 |
| 34 | Guatemala | 887.189559 |
| 92 | Nepal | 885.027129 |
| 37 | Guyana | 856.560366 |
| 89 | India | 800.872642 |
| 33 | El Salvador | 744.651946 |
| 63 | Eq. Guinea | 676.295484 |
| 151 | Djibouti | 673.087036 |
| 44 | Botswana | 663.699434 |
| 138 | Finland | 653.006309 |
| 67 | eSwatini | 623.384625 |
| 158 | Trinidad and Tobago | 585.450104 |
| 24 | Uruguay | 549.975775 |
| 62 | Gabon | 540.046438 |
| 134 | Philippines | 453.241689 |
| 41 | Jamaica | 427.779270 |
| 48 | Mauritania | 377.563707 |
| 36 | Venezuela | 361.362580 |
| 135 | Malaysia | 352.064968 |
| 90 | Bangladesh | 324.720973 |
| 7 | Indonesia | 282.109876 |
| 5 | Uzbekistan | 258.783034 |
| 75 | Algeria | 242.402612 |
| 93 | Pakistan | 234.093120 |
| 12 | Kenya | 202.141145 |
| 54 | Ghana | 199.167823 |
| 126 | Sri Lanka | 190.554126 |
| 73 | Gambia | 185.096446 |
| 142 | Japan | 182.893193 |
| 22 | Lesotho | 158.014997 |
| 95 | Tajikistan | 157.004353 |
| 94 | Afghanistan | 150.638197 |
| 148 | Egypt | 140.810481 |
| 61 | Congo | 139.423906 |
| 56 | Guinea-Bissau | 136.804554 |
| 46 | Senegal | 129.154117 |
| 64 | Zambia | 128.111695 |
| 125 | Australia | 122.264528 |
| 87 | South Korea | 118.676159 |
| 150 | Ethiopia | 117.566186 |
| 55 | Guinea | 110.416843 |
| 52 | Cameroon | 105.129509 |
| 42 | Cuba | 104.840525 |
| 31 | Nicaragua | 100.332711 |
| 43 | Zimbabwe | 98.695524 |
| 15 | Haiti | 93.916301 |
| 60 | Central African Rep. | 88.229260 |
| 152 | Uganda | 87.634295 |
| 91 | Bhutan | 86.378790 |
| 72 | Madagascar | 70.702827 |
| 66 | Mozambique | 69.561242 |
| 153 | Rwanda | 69.319087 |
| 98 | Syria | 62.922424 |
| 11 | Somalia | 62.591401 |
| 13 | Sudan | 62.432498 |
| 68 | Angola | 59.477440 |
| 124 | New Zealand | 47.934440 |
| 51 | Nigeria | 45.415188 |
| 53 | Togo | 45.335531 |
| 58 | Sierra Leone | 41.910081 |
| 88 | Mongolia | 39.599210 |
| 47 | Mali | 39.300552 |
| 57 | Liberia | 37.939689 |
| 136 | Brunei | 35.392804 |
| 65 | Malawi | 33.709716 |
| 59 | Burkina Faso | 32.977730 |
| 49 | Benin | 29.450652 |
| 159 | S. Sudan | 27.176147 |
| 141 | Eritrea | 21.152511 |
| 10 | Dem. Rep. Congo | 20.859256 |
| 14 | Chad | 17.199425 |
| 50 | Niger | 16.668967 |
| 6 | Papua New Guinea | 11.288477 |
| 84 | Thailand | 9.778681 |
| 144 | Yemen | 7.479448 |
| 69 | Burundi | 7.055178 |
| 127 | China | 6.951048 |
| 0 | Fiji | 5.320662 |
| 20 | Timor-Leste | 3.407266 |
| 83 | Cambodia | 2.258634 |
| 86 | Vietnam | 1.514141 |
| 1 | Tanzania | 0.943450 |
| 16 | Dominican Rep. | 0.819806 |
| 85 | Laos | 0.575301 |
| 82 | Vanuatu | 0.353589 |
geo_merged[['country', 'prevalence']].sort_values(by='prevalence', ascending=False).style.background_gradient('viridis')
| country | prevalence | |
|---|---|---|
| 118 | Luxembourg | 7812.263309 |
| 156 | Montenegro | 7436.308458 |
| 140 | Czechia | 6572.741981 |
| 29 | Panama | 6467.754218 |
| 78 | Qatar | 6205.788601 |
| 137 | Slovenia | 6071.620170 |
| 3 | United States of America | 6043.756661 |
| 119 | Belgium | 5606.323228 |
| 117 | Switzerland | 5438.180213 |
| 79 | Kuwait | 5226.989291 |
| 99 | Armenia | 5217.341047 |
| 70 | Israel | 5019.262128 |
| 108 | Lithuania | 4894.684898 |
| 116 | Croatia | 4856.509467 |
| 155 | Serbia | 4710.868646 |
| 120 | Netherlands | 4674.305735 |
| 133 | Georgia | 4585.421602 |
| 100 | Sweden | 4391.140714 |
| 106 | Moldova | 4140.356654 |
| 104 | Austria | 4088.246693 |
| 39 | France | 3960.327875 |
| 122 | Spain | 3901.735766 |
| 130 | United Kingdom | 3767.519910 |
| 81 | Oman | 3758.892835 |
| 121 | Portugal | 3746.025883 |
| 25 | Brazil | 3674.499830 |
| 8 | Argentina | 3643.729989 |
| 9 | Chile | 3411.888753 |
| 28 | Colombia | 3409.877002 |
| 30 | Costa Rica | 3409.841838 |
| 77 | United Arab Emirates | 3393.871527 |
| 128 | Italy | 3353.335543 |
| 103 | Poland | 3330.400876 |
| 27 | Peru | 3255.814673 |
| 105 | Hungary | 3243.813094 |
| 139 | Slovakia | 3180.929846 |
| 35 | Belize | 2976.028595 |
| 107 | Romania | 2916.590629 |
| 129 | Denmark | 2887.326104 |
| 154 | Bosnia and Herz. | 2878.106603 |
| 76 | Jordan | 2859.728989 |
| 71 | Lebanon | 2857.173126 |
| 112 | Bulgaria | 2833.481893 |
| 114 | Turkey | 2714.164345 |
| 157 | Kosovo | 2686.189157 |
| 102 | Ukraine | 2445.571789 |
| 18 | Bahamas | 2380.995673 |
| 132 | Azerbaijan | 2184.794179 |
| 17 | Russia | 2179.159331 |
| 110 | Estonia | 2177.725613 |
| 111 | Germany | 2160.399822 |
| 101 | Belarus | 2014.304672 |
| 109 | Latvia | 2007.720697 |
| 21 | South Africa | 1894.842436 |
| 115 | Albania | 1893.938524 |
| 146 | Cyprus | 1802.547421 |
| 123 | Ireland | 1799.145178 |
| 131 | Iceland | 1693.613189 |
| 2 | Canada | 1620.455832 |
| 143 | Paraguay | 1540.351675 |
| 80 | Iraq | 1516.738917 |
| 149 | Libya | 1502.056902 |
| 97 | Iran | 1485.893393 |
| 26 | Bolivia | 1421.877113 |
| 96 | Kyrgyzstan | 1396.463920 |
| 32 | Honduras | 1337.708426 |
| 40 | Ecuador | 1298.343438 |
| 147 | Morocco | 1286.775648 |
| 113 | Greece | 1280.756787 |
| 145 | Saudi Arabia | 1269.088334 |
| 74 | Tunisia | 1203.248040 |
| 23 | Mexico | 1135.008892 |
| 4 | Kazakhstan | 1080.165232 |
| 38 | Suriname | 1044.230714 |
| 45 | Namibia | 939.036856 |
| 19 | Norway | 921.232809 |
| 34 | Guatemala | 887.189559 |
| 92 | Nepal | 885.027129 |
| 37 | Guyana | 856.560366 |
| 89 | India | 800.872642 |
| 33 | El Salvador | 744.651946 |
| 63 | Eq. Guinea | 676.295484 |
| 151 | Djibouti | 673.087036 |
| 44 | Botswana | 663.699434 |
| 138 | Finland | 653.006309 |
| 67 | eSwatini | 623.384625 |
| 158 | Trinidad and Tobago | 585.450104 |
| 24 | Uruguay | 549.975775 |
| 62 | Gabon | 540.046438 |
| 134 | Philippines | 453.241689 |
| 41 | Jamaica | 427.779270 |
| 48 | Mauritania | 377.563707 |
| 36 | Venezuela | 361.362580 |
| 135 | Malaysia | 352.064968 |
| 90 | Bangladesh | 324.720973 |
| 7 | Indonesia | 282.109876 |
| 5 | Uzbekistan | 258.783034 |
| 75 | Algeria | 242.402612 |
| 93 | Pakistan | 234.093120 |
| 12 | Kenya | 202.141145 |
| 54 | Ghana | 199.167823 |
| 126 | Sri Lanka | 190.554126 |
| 73 | Gambia | 185.096446 |
| 142 | Japan | 182.893193 |
| 22 | Lesotho | 158.014997 |
| 95 | Tajikistan | 157.004353 |
| 94 | Afghanistan | 150.638197 |
| 148 | Egypt | 140.810481 |
| 61 | Congo | 139.423906 |
| 56 | Guinea-Bissau | 136.804554 |
| 46 | Senegal | 129.154117 |
| 64 | Zambia | 128.111695 |
| 125 | Australia | 122.264528 |
| 87 | South Korea | 118.676159 |
| 150 | Ethiopia | 117.566186 |
| 55 | Guinea | 110.416843 |
| 52 | Cameroon | 105.129509 |
| 42 | Cuba | 104.840525 |
| 31 | Nicaragua | 100.332711 |
| 43 | Zimbabwe | 98.695524 |
| 15 | Haiti | 93.916301 |
| 60 | Central African Rep. | 88.229260 |
| 152 | Uganda | 87.634295 |
| 91 | Bhutan | 86.378790 |
| 72 | Madagascar | 70.702827 |
| 66 | Mozambique | 69.561242 |
| 153 | Rwanda | 69.319087 |
| 98 | Syria | 62.922424 |
| 11 | Somalia | 62.591401 |
| 13 | Sudan | 62.432498 |
| 68 | Angola | 59.477440 |
| 124 | New Zealand | 47.934440 |
| 51 | Nigeria | 45.415188 |
| 53 | Togo | 45.335531 |
| 58 | Sierra Leone | 41.910081 |
| 88 | Mongolia | 39.599210 |
| 47 | Mali | 39.300552 |
| 57 | Liberia | 37.939689 |
| 136 | Brunei | 35.392804 |
| 65 | Malawi | 33.709716 |
| 59 | Burkina Faso | 32.977730 |
| 49 | Benin | 29.450652 |
| 159 | S. Sudan | 27.176147 |
| 141 | Eritrea | 21.152511 |
| 10 | Dem. Rep. Congo | 20.859256 |
| 14 | Chad | 17.199425 |
| 50 | Niger | 16.668967 |
| 6 | Papua New Guinea | 11.288477 |
| 84 | Thailand | 9.778681 |
| 144 | Yemen | 7.479448 |
| 69 | Burundi | 7.055178 |
| 127 | China | 6.951048 |
| 0 | Fiji | 5.320662 |
| 20 | Timor-Leste | 3.407266 |
| 83 | Cambodia | 2.258634 |
| 86 | Vietnam | 1.514141 |
| 1 | Tanzania | 0.943450 |
| 16 | Dominican Rep. | 0.819806 |
| 85 | Laos | 0.575301 |
| 82 | Vanuatu | 0.353589 |
geo_merged[['country', 'incidence']].sort_values(by='incidence', ascending=False).style.background_gradient('viridis')
| country | incidence | |
|---|---|---|
| 140 | Czechia | 153.821322 |
| 108 | Lithuania | 139.312905 |
| 137 | Slovenia | 123.470813 |
| 29 | Panama | 118.966988 |
| 100 | Sweden | 88.810919 |
| 110 | Estonia | 77.741672 |
| 130 | United Kingdom | 77.565887 |
| 109 | Latvia | 70.295679 |
| 3 | United States of America | 70.123673 |
| 117 | Switzerland | 65.854789 |
| 111 | Germany | 60.853152 |
| 156 | Montenegro | 58.828107 |
| 146 | Cyprus | 57.631745 |
| 139 | Slovakia | 55.840901 |
| 121 | Portugal | 55.805085 |
| 120 | Netherlands | 55.798401 |
| 118 | Luxembourg | 55.038460 |
| 70 | Israel | 50.435521 |
| 129 | Denmark | 49.732891 |
| 71 | Lebanon | 46.197354 |
| 155 | Serbia | 45.506807 |
| 116 | Croatia | 43.102494 |
| 39 | France | 39.510530 |
| 133 | Georgia | 35.279813 |
| 123 | Ireland | 34.283876 |
| 122 | Spain | 34.143441 |
| 103 | Poland | 33.215279 |
| 21 | South Africa | 32.293032 |
| 44 | Botswana | 30.475994 |
| 104 | Austria | 29.128167 |
| 106 | Moldova | 28.496417 |
| 77 | United Arab Emirates | 28.373933 |
| 25 | Brazil | 26.837757 |
| 30 | Costa Rica | 26.672032 |
| 8 | Argentina | 26.561583 |
| 128 | Italy | 26.074305 |
| 119 | Belgium | 24.635930 |
| 28 | Colombia | 24.401174 |
| 112 | Bulgaria | 24.346935 |
| 2 | Canada | 23.745441 |
| 107 | Romania | 22.642859 |
| 74 | Tunisia | 21.168382 |
| 101 | Belarus | 20.492689 |
| 105 | Hungary | 20.018587 |
| 114 | Turkey | 19.409930 |
| 45 | Namibia | 19.398096 |
| 99 | Armenia | 19.112102 |
| 102 | Ukraine | 19.085307 |
| 115 | Albania | 19.061761 |
| 17 | Russia | 18.343494 |
| 9 | Chile | 16.661732 |
| 76 | Jordan | 16.334785 |
| 35 | Belize | 15.540619 |
| 24 | Uruguay | 15.415988 |
| 67 | eSwatini | 14.449764 |
| 38 | Suriname | 14.022189 |
| 154 | Bosnia and Herz. | 13.770101 |
| 19 | Norway | 13.759282 |
| 26 | Bolivia | 13.332455 |
| 143 | Paraguay | 11.838003 |
| 132 | Azerbaijan | 10.560769 |
| 23 | Mexico | 9.958676 |
| 33 | El Salvador | 8.830185 |
| 149 | Libya | 8.792748 |
| 113 | Greece | 8.747755 |
| 78 | Qatar | 8.339429 |
| 138 | Finland | 7.976372 |
| 97 | Iran | 7.646770 |
| 40 | Ecuador | 7.280132 |
| 79 | Kuwait | 7.129388 |
| 147 | Morocco | 6.305416 |
| 135 | Malaysia | 5.958831 |
| 34 | Guatemala | 5.685371 |
| 48 | Mauritania | 5.374383 |
| 131 | Iceland | 5.298060 |
| 27 | Peru | 5.116531 |
| 4 | Kazakhstan | 4.785334 |
| 22 | Lesotho | 4.545357 |
| 62 | Gabon | 3.441943 |
| 18 | Bahamas | 3.333455 |
| 96 | Kyrgyzstan | 3.264744 |
| 7 | Indonesia | 3.070833 |
| 142 | Japan | 3.049393 |
| 126 | Sri Lanka | 2.882721 |
| 81 | Oman | 2.511399 |
| 91 | Bhutan | 2.505644 |
| 37 | Guyana | 2.439957 |
| 80 | Iraq | 2.298932 |
| 43 | Zimbabwe | 2.173112 |
| 63 | Eq. Guinea | 2.055609 |
| 87 | South Korea | 1.889362 |
| 64 | Zambia | 1.784373 |
| 92 | Nepal | 1.739024 |
| 89 | India | 1.702269 |
| 148 | Egypt | 1.454024 |
| 41 | Jamaica | 1.370980 |
| 47 | Mali | 1.302750 |
| 151 | Djibouti | 1.271284 |
| 93 | Pakistan | 1.207760 |
| 136 | Brunei | 1.127159 |
| 153 | Rwanda | 1.025082 |
| 152 | Uganda | 1.000755 |
| 134 | Philippines | 0.964932 |
| 46 | Senegal | 0.947607 |
| 36 | Venezuela | 0.830564 |
| 75 | Algeria | 0.788392 |
| 90 | Bangladesh | 0.782504 |
| 42 | Cuba | 0.771480 |
| 88 | Mongolia | 0.651839 |
| 98 | Syria | 0.560223 |
| 141 | Eritrea | 0.540639 |
| 51 | Nigeria | 0.532963 |
| 59 | Burkina Faso | 0.467487 |
| 150 | Ethiopia | 0.444233 |
| 65 | Malawi | 0.432376 |
| 66 | Mozambique | 0.425232 |
| 49 | Benin | 0.416712 |
| 158 | Trinidad and Tobago | 0.410439 |
| 145 | Saudi Arabia | 0.395495 |
| 15 | Haiti | 0.385095 |
| 95 | Tajikistan | 0.366060 |
| 54 | Ghana | 0.327274 |
| 11 | Somalia | 0.318666 |
| 5 | Uzbekistan | 0.262195 |
| 124 | New Zealand | 0.243885 |
| 14 | Chad | 0.240146 |
| 12 | Kenya | 0.235216 |
| 10 | Dem. Rep. Congo | 0.232890 |
| 58 | Sierra Leone | 0.227155 |
| 159 | S. Sudan | 0.222629 |
| 68 | Angola | 0.211530 |
| 94 | Afghanistan | 0.161173 |
| 55 | Guinea | 0.153055 |
| 125 | Australia | 0.103304 |
| 53 | Togo | 0.087884 |
| 60 | Central African Rep. | 0.035555 |
| 83 | Cambodia | 0.012342 |
| 127 | China | 0.005728 |
| 144 | Yemen | 0.003567 |
| 86 | Vietnam | 0.002080 |
| 52 | Cameroon | 0.000000 |
| 6 | Papua New Guinea | 0.000000 |
| 157 | Kosovo | 0.000000 |
| 13 | Sudan | 0.000000 |
| 16 | Dominican Rep. | 0.000000 |
| 20 | Timor-Leste | 0.000000 |
| 31 | Nicaragua | 0.000000 |
| 32 | Honduras | 0.000000 |
| 50 | Niger | 0.000000 |
| 1 | Tanzania | 0.000000 |
| 73 | Gambia | 0.000000 |
| 85 | Laos | 0.000000 |
| 56 | Guinea-Bissau | 0.000000 |
| 57 | Liberia | 0.000000 |
| 84 | Thailand | 0.000000 |
| 61 | Congo | 0.000000 |
| 82 | Vanuatu | 0.000000 |
| 69 | Burundi | 0.000000 |
| 72 | Madagascar | 0.000000 |
| 0 | Fiji | 0.000000 |
geo_merged.plot(cmap='rainbow', column='incidence', legend=True, figsize=(15,9), scheme='quantiles', k=3)
plt.title(f'SARS-Cov 2 incidence per 100000 inhabitants in the worldwide for date {end_date}.')
plt.show()
geo_merged[['country', 'new_cases']].sort_values(by='new_cases', ascending=False).style.background_gradient('viridis')
| country | new_cases | |
|---|---|---|
| 3 | United States of America | 229042 |
| 25 | Brazil | 55649 |
| 130 | United Kingdom | 50239 |
| 111 | Germany | 49044 |
| 39 | France | 26514 |
| 17 | Russia | 26095 |
| 89 | India | 21822 |
| 21 | South Africa | 17710 |
| 122 | Spain | 16716 |
| 140 | Czechia | 16420 |
| 128 | Italy | 16202 |
| 114 | Turkey | 15692 |
| 103 | Poland | 12780 |
| 23 | Mexico | 12406 |
| 8 | Argentina | 11765 |
| 28 | Colombia | 11639 |
| 120 | Netherlands | 9533 |
| 100 | Sweden | 8846 |
| 2 | Canada | 8459 |
| 102 | Ukraine | 8404 |
| 7 | Indonesia | 8002 |
| 97 | Iran | 6272 |
| 121 | Portugal | 6049 |
| 117 | Switzerland | 5424 |
| 107 | Romania | 4875 |
| 29 | Panama | 4465 |
| 70 | Israel | 4186 |
| 108 | Lithuania | 3934 |
| 142 | Japan | 3856 |
| 155 | Serbia | 3236 |
| 139 | Slovakia | 3041 |
| 9 | Chile | 2964 |
| 71 | Lebanon | 2878 |
| 119 | Belgium | 2831 |
| 129 | Denmark | 2788 |
| 104 | Austria | 2550 |
| 93 | Pakistan | 2475 |
| 137 | Slovenia | 2435 |
| 74 | Tunisia | 2414 |
| 147 | Morocco | 2143 |
| 105 | Hungary | 1972 |
| 101 | Belarus | 1957 |
| 135 | Malaysia | 1870 |
| 116 | Croatia | 1850 |
| 133 | Georgia | 1738 |
| 112 | Bulgaria | 1729 |
| 77 | United Arab Emirates | 1723 |
| 123 | Ireland | 1718 |
| 76 | Jordan | 1674 |
| 27 | Peru | 1588 |
| 26 | Bolivia | 1485 |
| 148 | Egypt | 1411 |
| 109 | Latvia | 1367 |
| 30 | Costa Rica | 1315 |
| 90 | Bangladesh | 1235 |
| 40 | Ecuador | 1186 |
| 132 | Azerbaijan | 1052 |
| 51 | Nigeria | 1016 |
| 134 | Philippines | 1006 |
| 106 | Moldova | 990 |
| 110 | Estonia | 973 |
| 87 | South Korea | 967 |
| 113 | Greece | 942 |
| 80 | Iraq | 901 |
| 4 | Kazakhstan | 888 |
| 34 | Guatemala | 879 |
| 143 | Paraguay | 822 |
| 19 | Norway | 732 |
| 146 | Cyprus | 704 |
| 44 | Botswana | 675 |
| 126 | Sri Lanka | 646 |
| 149 | Libya | 585 |
| 99 | Armenia | 582 |
| 115 | Albania | 581 |
| 33 | El Salvador | 545 |
| 154 | Bosnia and Herz. | 531 |
| 24 | Uruguay | 518 |
| 92 | Nepal | 511 |
| 45 | Namibia | 482 |
| 150 | Ethiopia | 468 |
| 138 | Finland | 438 |
| 152 | Uganda | 396 |
| 156 | Montenegro | 378 |
| 118 | Luxembourg | 327 |
| 75 | Algeria | 323 |
| 43 | Zimbabwe | 300 |
| 64 | Zambia | 285 |
| 36 | Venezuela | 260 |
| 47 | Mali | 233 |
| 67 | eSwatini | 212 |
| 79 | Kuwait | 205 |
| 48 | Mauritania | 202 |
| 10 | Dem. Rep. Congo | 194 |
| 78 | Qatar | 193 |
| 96 | Kyrgyzstan | 189 |
| 46 | Senegal | 139 |
| 153 | Rwanda | 122 |
| 145 | Saudi Arabia | 113 |
| 66 | Mozambique | 113 |
| 12 | Kenya | 112 |
| 98 | Syria | 101 |
| 59 | Burkina Faso | 94 |
| 54 | Ghana | 90 |
| 22 | Lesotho | 89 |
| 42 | Cuba | 86 |
| 81 | Oman | 86 |
| 38 | Suriname | 83 |
| 65 | Malawi | 83 |
| 127 | China | 79 |
| 5 | Uzbekistan | 78 |
| 68 | Angola | 62 |
| 62 | Gabon | 61 |
| 35 | Belize | 56 |
| 94 | Afghanistan | 55 |
| 49 | Benin | 46 |
| 15 | Haiti | 41 |
| 41 | Jamaica | 41 |
| 141 | Eritrea | 32 |
| 95 | Tajikistan | 31 |
| 159 | S. Sudan | 29 |
| 14 | Chad | 29 |
| 11 | Somalia | 24 |
| 125 | Australia | 24 |
| 88 | Mongolia | 20 |
| 91 | Bhutan | 19 |
| 55 | Guinea | 19 |
| 131 | Iceland | 18 |
| 37 | Guyana | 18 |
| 63 | Eq. Guinea | 16 |
| 58 | Sierra Leone | 14 |
| 124 | New Zealand | 11 |
| 151 | Djibouti | 11 |
| 18 | Bahamas | 11 |
| 53 | Togo | 7 |
| 136 | Brunei | 5 |
| 158 | Trinidad and Tobago | 5 |
| 60 | Central African Rep. | 2 |
| 83 | Cambodia | 2 |
| 86 | Vietnam | 2 |
| 144 | Yemen | 1 |
| 31 | Nicaragua | 0 |
| 20 | Timor-Leste | 0 |
| 6 | Papua New Guinea | 0 |
| 32 | Honduras | 0 |
| 157 | Kosovo | 0 |
| 13 | Sudan | 0 |
| 16 | Dominican Rep. | 0 |
| 56 | Guinea-Bissau | 0 |
| 50 | Niger | 0 |
| 52 | Cameroon | 0 |
| 57 | Liberia | 0 |
| 61 | Congo | 0 |
| 69 | Burundi | 0 |
| 72 | Madagascar | 0 |
| 73 | Gambia | 0 |
| 1 | Tanzania | 0 |
| 82 | Vanuatu | 0 |
| 84 | Thailand | 0 |
| 85 | Laos | 0 |
| 0 | Fiji | 0 |
geo_merged.plot(cmap='rainbow', column='new_cases', legend=True, figsize=(15,9), scheme='quantiles', k=3)
plt.title(f'SARS-Cov 2 new cases in the worldwide for date {end_date}.')
plt.show()
geo_merged[['country', 'new_fatalities']].sort_values(by='new_fatalities', ascending=False).style.background_gradient('viridis')
| country | new_fatalities | |
|---|---|---|
| 3 | United States of America | 3744 |
| 25 | Brazil | 1194 |
| 23 | Mexico | 1052 |
| 130 | United Kingdom | 982 |
| 111 | Germany | 963 |
| 17 | Russia | 585 |
| 128 | Italy | 575 |
| 103 | Poland | 565 |
| 21 | South Africa | 465 |
| 39 | France | 304 |
| 89 | India | 299 |
| 28 | Colombia | 289 |
| 102 | Ukraine | 257 |
| 114 | Turkey | 254 |
| 122 | Spain | 247 |
| 100 | Sweden | 243 |
| 7 | Indonesia | 241 |
| 97 | Iran | 149 |
| 8 | Argentina | 145 |
| 105 | Hungary | 137 |
| 140 | Czechia | 127 |
| 107 | Romania | 127 |
| 120 | Netherlands | 112 |
| 2 | Canada | 111 |
| 112 | Bulgaria | 110 |
| 117 | Switzerland | 101 |
| 104 | Austria | 90 |
| 139 | Slovakia | 82 |
| 119 | Belgium | 80 |
| 121 | Portugal | 79 |
| 108 | Lithuania | 75 |
| 134 | Philippines | 68 |
| 116 | Croatia | 65 |
| 113 | Greece | 58 |
| 93 | Pakistan | 58 |
| 148 | Egypt | 56 |
| 74 | Tunisia | 50 |
| 27 | Peru | 49 |
| 142 | Japan | 47 |
| 155 | Serbia | 44 |
| 29 | Panama | 42 |
| 147 | Morocco | 41 |
| 133 | Georgia | 38 |
| 132 | Azerbaijan | 34 |
| 137 | Slovenia | 34 |
| 129 | Denmark | 30 |
| 154 | Bosnia and Herz. | 26 |
| 71 | Lebanon | 26 |
| 106 | Moldova | 26 |
| 109 | Latvia | 23 |
| 143 | Paraguay | 22 |
| 34 | Guatemala | 22 |
| 90 | Bangladesh | 22 |
| 40 | Ecuador | 22 |
| 87 | South Korea | 21 |
| 70 | Israel | 15 |
| 149 | Libya | 15 |
| 30 | Costa Rica | 15 |
| 26 | Bolivia | 14 |
| 76 | Jordan | 14 |
| 33 | El Salvador | 14 |
| 123 | Ireland | 13 |
| 9 | Chile | 11 |
| 51 | Nigeria | 11 |
| 99 | Armenia | 10 |
| 145 | Saudi Arabia | 10 |
| 101 | Belarus | 10 |
| 67 | eSwatini | 9 |
| 98 | Syria | 8 |
| 80 | Iraq | 8 |
| 46 | Senegal | 7 |
| 92 | Nepal | 7 |
| 47 | Mali | 7 |
| 153 | Rwanda | 7 |
| 24 | Uruguay | 6 |
| 138 | Finland | 6 |
| 75 | Algeria | 6 |
| 135 | Malaysia | 6 |
| 118 | Luxembourg | 6 |
| 150 | Ethiopia | 5 |
| 110 | Estonia | 5 |
| 48 | Mauritania | 5 |
| 126 | Sri Lanka | 4 |
| 127 | China | 4 |
| 36 | Venezuela | 4 |
| 41 | Jamaica | 4 |
| 115 | Albania | 4 |
| 156 | Montenegro | 4 |
| 66 | Mozambique | 3 |
| 19 | Norway | 3 |
| 77 | United Arab Emirates | 3 |
| 11 | Somalia | 3 |
| 42 | Cuba | 2 |
| 12 | Kenya | 2 |
| 68 | Angola | 2 |
| 94 | Afghanistan | 2 |
| 59 | Burkina Faso | 2 |
| 146 | Cyprus | 2 |
| 152 | Uganda | 1 |
| 4 | Kazakhstan | 1 |
| 5 | Uzbekistan | 1 |
| 158 | Trinidad and Tobago | 1 |
| 65 | Malawi | 1 |
| 64 | Zambia | 1 |
| 79 | Kuwait | 1 |
| 43 | Zimbabwe | 1 |
| 96 | Kyrgyzstan | 1 |
| 35 | Belize | 1 |
| 45 | Namibia | 1 |
| 38 | Suriname | 1 |
| 124 | New Zealand | 0 |
| 151 | Djibouti | 0 |
| 136 | Brunei | 0 |
| 141 | Eritrea | 0 |
| 144 | Yemen | 0 |
| 157 | Kosovo | 0 |
| 131 | Iceland | 0 |
| 125 | Australia | 0 |
| 0 | Fiji | 0 |
| 95 | Tajikistan | 0 |
| 55 | Guinea | 0 |
| 53 | Togo | 0 |
| 52 | Cameroon | 0 |
| 50 | Niger | 0 |
| 49 | Benin | 0 |
| 44 | Botswana | 0 |
| 37 | Guyana | 0 |
| 32 | Honduras | 0 |
| 31 | Nicaragua | 0 |
| 22 | Lesotho | 0 |
| 20 | Timor-Leste | 0 |
| 18 | Bahamas | 0 |
| 16 | Dominican Rep. | 0 |
| 15 | Haiti | 0 |
| 14 | Chad | 0 |
| 13 | Sudan | 0 |
| 10 | Dem. Rep. Congo | 0 |
| 6 | Papua New Guinea | 0 |
| 54 | Ghana | 0 |
| 56 | Guinea-Bissau | 0 |
| 91 | Bhutan | 0 |
| 57 | Liberia | 0 |
| 88 | Mongolia | 0 |
| 86 | Vietnam | 0 |
| 85 | Laos | 0 |
| 84 | Thailand | 0 |
| 83 | Cambodia | 0 |
| 82 | Vanuatu | 0 |
| 81 | Oman | 0 |
| 1 | Tanzania | 0 |
| 78 | Qatar | 0 |
| 73 | Gambia | 0 |
| 72 | Madagascar | 0 |
| 69 | Burundi | 0 |
| 63 | Eq. Guinea | 0 |
| 62 | Gabon | 0 |
| 61 | Congo | 0 |
| 60 | Central African Rep. | 0 |
| 58 | Sierra Leone | 0 |
| 159 | S. Sudan | 0 |
geo_merged.plot(cmap='OrRd', column='new_fatalities', legend=True, figsize=(15,9), scheme='quantiles', k=2)
plt.title(f'SARS-Cov 2 new fatalities in the worldwide for date {end_date}.')
plt.show()
The case fatality rate is the proportion of deaths related to a particular disease or condition, out of the total number of cases affected by the disease or affected by the particular condition.
geo_merged[['country', 'lethality']].sort_values(by='lethality', ascending=False).style.background_gradient('viridis')
| country | lethality | |
|---|---|---|
| 144 | Yemen | 29.089175 |
| 23 | Mexico | 8.833291 |
| 40 | Ecuador | 6.629884 |
| 13 | Sudan | 6.296106 |
| 98 | Syria | 6.205924 |
| 26 | Bolivia | 5.776905 |
| 148 | Egypt | 5.544334 |
| 14 | Chad | 5.007222 |
| 127 | China | 4.986649 |
| 57 | Liberia | 4.665542 |
| 97 | Iran | 4.520604 |
| 94 | Afghanistan | 4.242778 |
| 1 | Tanzania | 4.125737 |
| 0 | Fiji | 4.081633 |
| 47 | Mali | 3.827002 |
| 112 | Bulgaria | 3.734718 |
| 27 | Peru | 3.718372 |
| 154 | Bosnia and Herz. | 3.649142 |
| 128 | Italy | 3.532389 |
| 34 | Guatemala | 3.501597 |
| 113 | Greece | 3.471628 |
| 74 | Tunisia | 3.366954 |
| 10 | Dem. Rep. Congo | 3.360958 |
| 73 | Gambia | 3.265736 |
| 125 | Australia | 3.200141 |
| 50 | Niger | 3.179551 |
| 119 | Belgium | 3.017655 |
| 7 | Indonesia | 2.985075 |
| 130 | United Kingdom | 2.977499 |
| 105 | Hungary | 2.950777 |
| 58 | Sierra Leone | 2.942315 |
| 65 | Malawi | 2.920723 |
| 33 | El Salvador | 2.887293 |
| 75 | Algeria | 2.770086 |
| 11 | Somalia | 2.757743 |
| 31 | Nicaragua | 2.729077 |
| 9 | Chile | 2.718346 |
| 21 | South Africa | 2.697657 |
| 2 | Canada | 2.684724 |
| 8 | Argentina | 2.674407 |
| 122 | Spain | 2.653571 |
| 43 | Zimbabwe | 2.642202 |
| 28 | Colombia | 2.638182 |
| 157 | Kosovo | 2.602632 |
| 37 | Guyana | 2.595347 |
| 32 | Honduras | 2.572946 |
| 25 | Brazil | 2.544558 |
| 107 | Romania | 2.483673 |
| 123 | Ireland | 2.469026 |
| 39 | France | 2.427281 |
| 86 | Vietnam | 2.403846 |
| 48 | Mauritania | 2.388838 |
| 41 | Jamaica | 2.360666 |
| 15 | Haiti | 2.360236 |
| 68 | Angola | 2.323180 |
| 35 | Belize | 2.256621 |
| 137 | Slovenia | 2.225656 |
| 103 | Poland | 2.186569 |
| 18 | Bahamas | 2.163676 |
| 80 | Iraq | 2.154626 |
| 46 | Senegal | 2.121932 |
| 93 | Pakistan | 2.106459 |
| 143 | Paraguay | 2.096150 |
| 106 | Moldova | 2.057828 |
| 115 | Albania | 2.033710 |
| 67 | eSwatini | 2.011808 |
| 100 | Sweden | 1.995295 |
| 38 | Suriname | 1.957612 |
| 134 | Philippines | 1.953307 |
| 136 | Brunei | 1.910828 |
| 111 | Germany | 1.908505 |
| 64 | Zambia | 1.886424 |
| 53 | Togo | 1.883135 |
| 116 | Croatia | 1.851799 |
| 56 | Guinea-Bissau | 1.835237 |
| 17 | Russia | 1.796506 |
| 159 | S. Sudan | 1.779661 |
| 102 | Ukraine | 1.769742 |
| 99 | Armenia | 1.766764 |
| 158 | Trinidad and Tobago | 1.766685 |
| 3 | United States of America | 1.734062 |
| 12 | Kenya | 1.731930 |
| 104 | Austria | 1.718068 |
| 145 | Saudi Arabia | 1.713729 |
| 52 | Cameroon | 1.704913 |
| 117 | Switzerland | 1.695449 |
| 121 | Portugal | 1.682055 |
| 147 | Morocco | 1.681789 |
| 96 | Kyrgyzstan | 1.674851 |
| 22 | Lesotho | 1.648352 |
| 29 | Panama | 1.637528 |
| 63 | Eq. Guinea | 1.633739 |
| 140 | Czechia | 1.628940 |
| 109 | Latvia | 1.603360 |
| 138 | Finland | 1.550561 |
| 61 | Congo | 1.548929 |
| 150 | Ethiopia | 1.548573 |
| 87 | South Korea | 1.481725 |
| 51 | Nigeria | 1.476160 |
| 72 | Madagascar | 1.473411 |
| 90 | Bangladesh | 1.469475 |
| 149 | Libya | 1.459949 |
| 89 | India | 1.448746 |
| 120 | Netherlands | 1.429641 |
| 156 | Montenegro | 1.425223 |
| 142 | Japan | 1.402251 |
| 4 | Kazakhstan | 1.377449 |
| 49 | Benin | 1.353430 |
| 76 | Jordan | 1.301750 |
| 30 | Costa Rica | 1.291386 |
| 60 | Central African Rep. | 1.269394 |
| 59 | Burkina Faso | 1.266777 |
| 42 | Cuba | 1.240695 |
| 132 | Azerbaijan | 1.198791 |
| 139 | Slovakia | 1.192071 |
| 81 | Oman | 1.162998 |
| 124 | New Zealand | 1.156337 |
| 6 | Papua New Guinea | 1.153846 |
| 133 | Georgia | 1.098308 |
| 118 | Luxembourg | 1.066466 |
| 151 | Djibouti | 1.047390 |
| 153 | Rwanda | 1.042424 |
| 108 | Lithuania | 1.028802 |
| 155 | Serbia | 0.944204 |
| 24 | Uruguay | 0.941558 |
| 114 | Turkey | 0.940722 |
| 84 | Thailand | 0.911809 |
| 36 | Venezuela | 0.906109 |
| 66 | Mozambique | 0.892616 |
| 19 | Norway | 0.889614 |
| 45 | Namibia | 0.840012 |
| 110 | Estonia | 0.829175 |
| 71 | Lebanon | 0.817996 |
| 5 | Uzbekistan | 0.797558 |
| 70 | Israel | 0.793837 |
| 129 | Denmark | 0.775970 |
| 101 | Belarus | 0.735076 |
| 152 | Uganda | 0.723823 |
| 92 | Nepal | 0.710223 |
| 95 | Tajikistan | 0.676895 |
| 62 | Gabon | 0.668687 |
| 79 | Kuwait | 0.620767 |
| 54 | Ghana | 0.611638 |
| 55 | Guinea | 0.583643 |
| 146 | Cyprus | 0.540442 |
| 131 | Iceland | 0.503997 |
| 126 | Sri Lanka | 0.466020 |
| 135 | Malaysia | 0.419061 |
| 77 | United Arab Emirates | 0.322671 |
| 44 | Botswana | 0.272109 |
| 69 | Burundi | 0.247219 |
| 78 | Qatar | 0.170588 |
| 88 | Mongolia | 0.082305 |
| 141 | Eritrea | 0.079872 |
| 16 | Dominican Rep. | 0.000000 |
| 91 | Bhutan | 0.000000 |
| 20 | Timor-Leste | 0.000000 |
| 82 | Vanuatu | 0.000000 |
| 83 | Cambodia | 0.000000 |
| 85 | Laos | 0.000000 |
geo_merged.plot(cmap='OrRd', column='lethality', legend=True, figsize=(15,9), scheme='quantiles', k=3)
plt.title(f'SARS-Cov 2 lethality in the worldwide for date {end_date}.')
plt.show()